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Development of an Interactive Medical Knowledge Graph Based Tool Set

Procedia Computer Science 2023年 221卷 578-584页

作者： Xiaowei Xu xuwen wang Meng Wu Hetong Ma Liu Shen Jiao Li Institute of Medical Information & Library Chinese Academy of Medical Sciences& Peking Union Medical College Beijing 100020 China Key Laboratory of Medical Information Intelligent Technology Chinese Academy of Medical Sciences Beijing 100020 China

The field of clinical research has reached a turning point in terms of the range and diversity of available data and their potential to improve human health and well-being. However, due to discipline-specific variances in terminology and representation, the data is often compartmentalized, disorganized, and inaccessible to a broad audience. In response to these challenges, we designed and evaluated a pilot knowledge graph-based MedKaaS tool capable of integrating existing clinical datasets and translating those data into insights intended to augment human reasoning and accelerate scientific research. In this paper, we present the architecture and performance of our system, which has been applied to several real-world use cases in collaboration with subject-matter specialists. The MedKaaS Tools are actively developed, with regular updates to performance and features. Our future plans include developing a more user-friendly interface and expanding the visualization capabilities for knowledge exploration.

关键词： knowledge graph visualization application programming interface biomedical data

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Size and Charge Dual-Switchable Nanoparticles for Achieving Chemosensitization and Immune Infiltration against Pancreatic Ductal Adenocarcinoma

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ADVANCED FUNCTIONAL MATERIALS 2025年第1期35卷

作者： Song, Haolin Chen, Hongyi Chen, Qinjun Fan, Hongrui Li, Chufeng wang, Yu Zhang, Shilin Li, xuwen Su, Boyu Sun, Tao Jiang, Chen Fudan Univ Sch Pharm Dept Pharmaceut Key Lab Smart Drug DeliveryMinist EducState Key Shanghai 201203 Peoples R China MOE Frontiers Ctr Brain Sci Shanghai 201203 Peoples R China Fujian Med Univ Affiliated Hosp 1 Natl Reg Med Ctr Dept Digest Dis Binhai Campus Fuzhou 350212 Peoples R China

Pancreatic ductal adenocarcinoma (PDAC), one of the most challenging cancers, is uniquely characterized by a biological barrier composed of multiple components in the extracellular matrix, preventing penetration of chemotherapeutic agents and hindering clinical drug treatment. Collagen I arrangement is critical. In this study, gemcitabine is chosen as the chemotherapeutic agent and dasatinib as the collagen-I-disrupting agent. When collagen I arrangement is disrupted, not only the chemotherapeutic drug enters the tumor area better, but the infiltration of CD8+ T cells into the tumor also increases. Therefore, a hypoxia-responsive linker-crosslinked polymer, DGD/L@GBI, is designed, which can be simultaneously loaded with two therapeutic agents, gemcitabine and dasatinib. The polymer is encapsulated by nucleic acid aptamers that target the PDAC stromal microenvironment and shield the surface. The aptamer is removed upon arrival at the PDAC hypoxic microenvironment, exposing the positively charged core to achieve charge reversal. Particle size transformation is achieved using linkers that respond to a hypoxic microenvironment. Increased intratumoral penetration is achieved using the dual transformation capabilities of the formulation. Pairing these two agents can result in a powerful efficacy in chemotherapy sensitization and immune infiltration.

关键词： charge reversal chemosensitization immune infiltration pancreatic ductal adenocarcinoma size reduction

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FilterLAP: Filtering False-positive Mutation Calls via a Label Propagation Framework

FilterLAP: Filtering False-positive Mutation Calls via a Lab...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： xuwen wang Xiaoyan Zhu Xiao Xiao Shenjie wang Xuanping Zhang Xin Lai Jiayin wang Shaanxi Engineering Research Center of Medical and Health Big Data Xi'an Jiaotong UniversityXi'anShaanxiChina710049 School of Pub

ISBN: (纸本)9781728118680

Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the false-positive calls. The existing filters rely on the whole genome/exome sequencing data, which can provide sufficient samples for training the filters. However, the gene-panel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semi-learning filter for gene-panel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on gene-panel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.90-0.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on gene-panel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for gene-panel sequencing data.

关键词： cancer data analysis genetics genomics graph theory inference mechanisms learning (artificial intelligence) medical diagnostic computing probability medical diagnostic computing Inference mechanisms Genomics Graph theory calls Filters Data analysis Analyses, Sequence Genetics Probability Filtration adjacent nodes Probability distribution

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利用粗糙集和支持向量机的银行借贷风险预测模型

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成都理工大学学报（自然科学版） 2022年第2期49卷 249-256页

作者：吴尚智王旭文王志宁任艺璇西北师范大学计算机科学与工程学院兰州730070

将粗糙集和支持向量机相结合,提出利用粗糙集和支持向量机的银行借贷风险预测模型,提高银行个人借贷风险预测精度,为银行判断是否同意借贷提供参考和依据。该模型首先将银行样本数据形成初始决策表,对其进行数据预处理;然后用属性相似... 详细信息

将粗糙集和支持向量机相结合,提出利用粗糙集和支持向量机的银行借贷风险预测模型,提高银行个人借贷风险预测精度,为银行判断是否同意借贷提供参考和依据。该模型首先将银行样本数据形成初始决策表,对其进行数据预处理;然后用属性相似度的属性约简算法对决策表进行属性约简,去除冗余属性,获得高效的决策规则;最后将约简后的最小属性作为4种不同核函数的支持向量机学习样本,构建预测模型且进行预测评价对比分析。实验结果表明,使用相同训练样本,高斯核函数的支持向量机预测模型的准确率最高且为93%,说明该预测模型具有较高的精度。通过实验分析,构建预测模型有效地预测了银行借贷风险,且为银行借贷管理提供辅助决策。

关键词：粗糙集属性约简支持向量机核函数预测模型

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TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples

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BRIEFINGS IN BIOINFORMATICS 2024年第3期25卷 bbae159-bbae159页

作者： wang, Shenjie Zhu, Xiaoyan wang, xuwen Liu, Yuqian Zhao, Minchao Chang, Zhili wang, Xiaonan Shao, Yang wang, Jiayin Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Nanjing Geneseeq Technol Inc Nanjing Jiangsu Peoples R China

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://***/hello-json/TMBstable for academic usage only.

关键词： sequencing data analysis variant calling counting-based biomarker tumor mutation burden immunotherapy meta-learning approach

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The Supercritical N-Butanol Degradation Process for Recycling Photovoltaic Modules

SSRN

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SSRN 2024年

作者： wang, xuwen Liu, Zhifeng Ruan, Haoda Li, Xinyu Zhang, Lei School of Mechanical Engineering Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Low Carbon Recycling Technology and Equipment for Mechanical and Electrical Products Hefei230009 China State Key Laboratory of Environmental Adaptability for lndustrial Products Guangzhou510300 China

The rapid advancement of the photovoltaic (PV) industry has elevated the recycling and reuse of decommissioned PV modules to critical challenges, essential for tackling environmental concerns and achieving resource reutilization. To facilitate the efficient degradation of PV modules and recycling of silicon materials, this study proposes a PV module degradation and recycling process based on supercritical n-butanol. The effects of various factors on the degradation rate of PV modules were systematically evaluated using single-factor studies to select an appropriate range of process parameters for optimization. Employing the response surface methodology (RSM) for the meticulous optimization of process parameters, the optimized parameters were as follows: n-butanol solvent volume of 470 ml, temperature of 328 °C for 83 minutes, resulting in a module degradation rate of up to 99 %. A second-order regression model was established to predict degradation efficiency. The high applicability of the fitted model was verified by comparing the actual degradation rates with the model's predicted values. Finally, the superiority of this recycling method was validated by characterizing the solar cells obtained through the supercritical method and comparing these characteristics with those of cells disassembled mechanically. © 2024, The Authors. All rights reserved.

关键词： Recycling

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Development and Validation of a Screening Tool for Generalized Anxiety and Major Depressive Disorder in Patients with Chronic Obstructive Pulmonary Disease

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LUNG 2025年第1期203卷 1-12页

作者： Liu, Meishan Yang, xuwen wang, Dong Fang, Jiexin Li, Boyu An, Li Chang, Yuhan Liu, Haiman Hu, Yongdong Huang, Kewu Capital Med Univ Beijing Inst Resp Med Dept Resp & Crit Care Med 8Gongti South Rd Beijing 100020 Peoples R China Capital Med Univ Beijing Chao Yang Hosp 8Gongti South Rd Beijing 100020 Peoples R China Capital Med Univ Beijing Chao Yang Hosp Dept Clin Psychol 8Gongti South Rd Beijing 100020 Peoples R China Capital Med Univ Beijing Chao Yang Hosp Dept Pharm Beijing Peoples R China

PurposeAnxiety and depression are often underdiagnosed in patients with chronic obstructive pulmonary disease (COPD). This study aimed to develop and validate a screening tool for anxiety and depression in COPD *** COPD patients were consecutively recruited from November 2021 to October 2023 and underwent a psychiatric interview to diagnose generalized anxiety disorder (GAD) and/or major depressive disorder (MDD) based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Patients were split into training and validation sets according to their recruitment time. We assessed known risk factors and used core items from the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) to develop a prediction nomogram. Multivariable logistic regression with least absolute shrinkage and selection operator (LASSO) were used to construct the *** the enrolled COPD patients (n = 329), 58 (25.6%) in the training cohort and 33 (32.4%) in the validation cohort were diagnosed with GAD and/or MDD. Three variables were identified in the prediction nomogram: COPD Assessment Test score and two core items from PHQ-ADS. The under the curve (AUC) value for the nomogram was 0.826 (95% CI: 0.755-0.897) and 0.855 (95% CI: 0.767-0.942) in the training and validation cohorts, respectively. The calibration curve was close to the diagonal. The discriminatory power of the screening nomogram was comparable to that of PHQ-ADS (AUC: 0.826 vs. 0.831, P = 0.832).ConclusionThe new screening tool for GAD and MDD in COPD patients is concise and valid, with discriminatory power comparable to existing anxiety/depression screening questionnaires.

关键词： Chronic obstructive pulmonary disease Anxiety Depression Screening Nomogram

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GSDcreator: An Efficient and Comprehensive Simulator for Genarating NGS Data with Population Genetic Information

GSDcreator: An Efficient and Comprehensive Simulator for Gen...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Shenjie wang Jiayin wang Xiao Xiao Xuanping Zhang xuwen wang Xiaoyan Zhu Xin Lai School of Computer Science and Technology Xi'an Jiaotong University School of Public Policy and Administration Xi'an Jiaotong University

ISBN: (纸本)9781728118680

In recent decades, NGS data analysis has become a major research field in bioinformatics, which presents great advantages in many application scenarios. Many algorithms and software were designed for analyzing the NGS data, while simulation datasets are urgently needed for testing software and optimizing their parameter configurations. Thus, a series of NGS data simulators have been published. However, the existing simulators cannot satisfy the requirements from many specific scenarios. First, they do not support many newly discovered variations. Second, complex structural variations are difficult to generate. In addition, along with the increase of population data, it is urgent to increase population information simulation. In this paper, we propose GSDcreator, a comprehensive NGS simulator that overcome the three weaknesses mentioned above. It can produce all known types of variation, where the complex of variations are also supported. Furthermore, it can capture many important real data features including population polymorphism, insert size distribution, adjacent site depth distribution, overall depth distribution, quality score distribution, amplification bias, sequencing errors and so on. It's highlighted that 1000 Genomes Project Database is taken as a reference and integrates population genetic information to simulate population polymorphism. To test the performance, we did a lot of experiments and found that simulated data produced by GSDcreator are quit mimic to the real sequencing data.

关键词： population genomics next-generation sequencing data analysis data simulator population information Metagenomics simulator testing software population genetics Population statistics simulated data Bioinformatics

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成品油管道顺序输送中混油界面检测方法的研究

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石化技术 2020年第4期27卷 108-109页

作者：王绪文中国石油工程项目管理公司天津分公司天津300457

不同标号的成品油在通过外输管道交替输送的过程中都会产生混油。混油的出现将会降低油品品质,还会增加油品处理成本,及时准确掌握混油界面情况,对安全生产具有重大意义。本文介绍一种近年来在油品分析领域成功应用的一种新型技术--信... 详细信息

不同标号的成品油在通过外输管道交替输送的过程中都会产生混油。混油的出现将会降低油品品质,还会增加油品处理成本,及时准确掌握混油界面情况,对安全生产具有重大意义。本文介绍一种近年来在油品分析领域成功应用的一种新型技术--信息融合技术,阐述其基本原理及在工程中的应用。

关键词：成品油管道顺序输送混油信息融合技术

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阿米替林联合多潘立酮治疗功能性消化不良的临床疗效分析

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中国继续医学教育 2015年第8期7卷 229-230页

作者：王旭文黑龙江省双鸭山市煤炭总医院集贤分院 155141

目的观察多潘立酮联合阿米替林治疗功能性消化不良临床疗效。方法对照组给予多潘立酮;观察组给予多潘立酮加阿米替林。连续治疗1个月。比较两组临床疗效和症状变化。结果观察组总有效率显著优于对照组(P<0.05);治疗后,指标评分明显... 详细信息

目的观察多潘立酮联合阿米替林治疗功能性消化不良临床疗效。方法对照组给予多潘立酮;观察组给予多潘立酮加阿米替林。连续治疗1个月。比较两组临床疗效和症状变化。结果观察组总有效率显著优于对照组(P<0.05);治疗后,指标评分明显优于对照组(P<0.05)。结论多潘立酮联合阿米替林治疗功能性消化不良临床疗效良好。

关键词：多潘立酮阿米替林功能性消化不良

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